SRC and TP53 play critical role in low-grade dysplasia colorectal mucosa transformation into cancer

Aim: Determination of crucial genes that are involved in onset and progress of dysplasia of colorectal mucosa is the aim of this study. Background: Management of dysplasia as one of the risk factors of colon cancer is very challenging. Molecular studies could be helpful in this matter. Here, the transcriptome profile of low-grade dysplasia in colon tissue in comparison with normal one is studied by protein-protein interaction (PPI) network analysis. Methods: For detecting differentially expressed genes (DEGs) of dysplasia lesion, the data was downloaded from the gene chip GSE31106, platform GPL1261, GSM770092-94 as normal colorectal mucosa group and GSM770098-100 as low-grade dysplasia colorectal mucosa from the Gene Expression Omnibus database (GEO). The expression profile is evaluated by GEO2R and a network of DEGs is constructed and analyzed by Cytoscape algorithms. Results: The findings indicate that a PPI network analysis of 113 DEGs is consist of 8 nodes that 6 of them are common with inflammation state. Only SRC and TP53 were recognized as the specific makers for dysplasia. In this respect, a subnetwork of these two genes introduce a panel of 8 nodes consist of HRAS, MYC, PIK3CA, PIK3CB, PIK3CD, PIK3CG, SRC, and TP53. Conclusion: It can be concluded that SRC and TP53 may play prominent role in dysplasia pathogenicity after running validation tests.


Introduction
1 Cancer of colon is the most frequent cause of cancer death worldly (1). Long-standing inflammatory bowel disease (IBD) is a risk factor for colon cancer as two different types including ulcerative colitis (UC) and Crohn's disease (2). In addition, prior to tumor formation, dysplasia lesions could develop from IBD with different grades known as low and high grades (3). In view of this fact, the cancer management requires precancerous dysplasia identification via surveillance approaches known as colonoscopy (4). However, it is known that dysplasia detection is complicated especially for the low-grade type (5). The reason for this is due to hardship of differentiate from normal tissue and their multifocal properties (6). A requirement for establishing methods of accurate and less invasive ORIGINAL ARTICLE approaches for the diagnosis of low-grade dysplasia for the treatment approaches, attracted researchers to the molecular studies. This can be achievable through highthroughput investigations. In this respect, sequential analysis of tumorigenesis from inflammation to the tumor could assist improving the understanding the underlying mechanisms from molecular perspective (7). One way to assist this, is application of bioinformatic study such as interaction analysis. Interaction mapping could promote introducing the most prominent biomarkers of the disease (8). In fact, aiming toward a better understanding of dysplasia and ultimately tumorigenesis from molecular view (7), suggested protein-protein interaction (PPI) network analysis by this research. In this approach, the involved genes in disorder were screened and the prominent ones introduced as possible biomarkers (9,10). In other words, biomarker discovery in dysplasia could be helpful for the prediction of colon cancer. Here, the interactome profile of dysplasia lesion is compared and evaluated with normal condition.

Data collection
Series GSE31106, platform GPL1261, GSM770092-94 as normal colorectal mucosa group and GSM770098-100 as low-grade dysplasia colorectal mucosa group were downloaded from GEO database. 10mg/kg Azoxymethane was intraperitoneal injected to the five-week-old male mice. The mice were treated with three cycles of Dextran sulfate sodium (2%, 1.5%, and 1.5%). Saline injection and distilled water drinking were applied for normal control group. Microscopic assessment of colorectal tissue confirmed low-grade dysplasia in treated samples related to the normal group. Affymerix GeneChip Mouse Genome 430 2.0 Array was used to evaluate extracted RNAs of tissue. Based on p-value ≤ 0.05 and 2 ≤ fold change ≤ 0.5 the 250 top DEGs were selected to further analysis.

PPI network analysis
The characterized and significant DEGs were interacted by STRING (11) database plugin of Cytoscape software v 6.3.2 (12). The constructed network was analyzed by Network Analyzer application of Cytoscape and hubs are determined as the nodes with degree value ≥ (mean+2SD) (13). Since similar research is dealing about mice that are treated like these mice but investigated two weeks later and were diagnosed as inflamed colorectal samples, the hubs and 50 relative genes which probably differentiate dysplasia network from inflamed one were included in a PPI sub-network. The constructed sub-network was analyzed to introduce possible mechanism of transition from inflamed colorectal to low grade dysplasia tissue.

Statistical analysis
Gene expression profiles were matched via boxplot analysis and p-value <0.05 was considered for significant findings.

Results
Gene profiles of the normal controls and low-grade dysplasia were matched via boxplot analysis to promote more exploration (Figure 1). Among 250 top significant DEGs, 158 individuals were characterized which were included in the constructed PPI network. A number of 113 DEGs were recognized by STRING database and 45 ones were not documented. The network was created by 113 DEGs and 50 related genes. The network included 40 isolated nodes, one paired genes and a main connected component which was characterized with 121 nodes and 1190 edges. The network was analyzed and it was confirmed that the built network is scale free. Degree distribution of network ( Figure 2) was fitted to equation y = ax b where a and b were 8.615 and -0.464, respectively.

Discussion
Molecular studies showed promising for understanding disease behavior and mechanisms of which, Introduction of biomarkers could be helpful for diagnosis, screening, and treatment protocols. One way is the study of genome profile of disease state in comparison with the normal condition to recognize the differentially expressed genes. These genes could also be more promising in terms of biomarker study, if further evaluated for their interaction properties in a whole interaction system known as PPI map (15). Here, we followed this procedure and genes that were modified in expression in dysplasia lesion were analyzed and used for interactomic investigation. What is more, low-grade dysplasia lesions are important to be examined for their valuable asset of being potential for developing cancerous condition (16). It is durable to detect low-grade dysplasia, in this respect, molecular biomarker discovery can provide novel information and facilitate both the detection and intervention approaches. For perusing this goal, in this conducted research, we first analyzed and compared the groups of samples in terms of expression values and it was concluded that these groups are comparable statistically as demonstrated in figure 1. The next step was the determination of DEGs and the construction a network of protein interaction. The DEGS shaped a network in which, 8 nodes declared central values known as hubbottleneck elements. These 8 nodes includes PRDM10, GAPDH, INS, AKT1, SRC, IL6, ALB, and TP53 that among them 6 ones are common with inflammation based on the previous study conducted by the same research group. SRC and TP53 are the uncommon ones that apparently specific to dysplasia. It seems that SRC and TP53 act in the opposite manner including tumorigenesis and tumor suppressor activity respectively. Therefore, elevation of SRC and decrement of TP53 are expected. Expression change of these two important genes in low-grade dysplasia can be interpreted as potential of dysplasia to convert into colon cancer. To gain a better view of possible involved biomarkers of dysplasia, a sub-network of the 2 probable agents was constructed and some other genes are explored. The second set of hub-bottlenecks   (20). Taking together this information can implies that the most of these genes are the famous parts of cancer pathogenicity. HRAS, MYC, and TP53 are known for their exceptional role in cancer growth and are studied highly in this field. Thus, these common biomarkers of cancer condition are also present in our sub-network of dysplasia. This may show that the dysplasia is in high risk for cancer trigger. A deeper look to the suggested panel may imply that quantities analysis of PIK3C, SRC, and TP53 expression be required as useful tool to follow up of dysplasia patients to prevent progress of lesion into cancer (21)(22)(23)(24). In summary, PIK3C, SRC, and TP53 genes appeared as important common agents between low grade dysplasia and colon cancer. Due to participation of these genes in many different types of cancers, quantity analysis of gene expression of PIK3C, SRC, and TP53 in dysplasia colorectal condition can be beneficial.